Targeted ratio of signal power to interference plus noise power for enhancement of a multi-user detection receiver

ABSTRACT

According to some embodiments, in a multi-user detection (MUD) receiver, a method for identifying a beam which produces a specific signal-to-interference-plus-noise ratio (SINR) can include: determining a maximum output SINR; determining beam weights to achieve a target SINR using the determined maximum output SINR; applying the beam weights to one or more received signals to generate a beamformed signal having the target SINR, one or more of the received signals having a signal of interest (SOI), one or more interfering signals, and noise; and providing the beamformed signal to a multi-user detection unit to recover the SOI.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 119 of provisionalpatent application No. 62/861,569 filed Jun. 14, 2019, which is herebyincorporated by reference herein in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with Government support under Grant No.FA8702-15-D-0001 awarded by the U.S. Air Force. The Government hascertain rights in the invention.

FIELD

The disclosure pertains generally to wireless communication, and moreparticularly to cognitive radio systems and techniques for achieving adhoc wireless communications in the presence of other user interference(sometime referred to herein as “interference multiple access wirelesscommunications”).

BACKGROUND

As is known in the art, different wireless networks and/or systems ofradios avoid interfering with each other by various options. Forexample, some systems rely on pre-arrangement or careful assignment offrequency bands, time slots, or signature pulses as is done for cellularsystems through frequency reuse maps and TDMA for GSM, OFDMA for LTE,spread spectrum for IS-95, and combinations of these for WCDMA throughHSPA commercial cellular standards. Other systems utilize collisionavoidance techniques such as those employed for a packet based systemssuch as 802.11/16/22 (WiFi and WiMax) where collisions are controlled aspart of a multiple access medium access control procedure (E.g. carriersense multiple access). Still other systems utilize techniques for “onthe fly” interference assessment and avoidance, such as dynamic spectrumaccess (DSA). This is done by the system of “secondary user” radiosactively sensing the radio spectrum and coordinating to choose an emptyband for transmission. Existing systems, however, fail if they areunable to avoid interference.

As the consumer market continues to rise for smart phones and wirelessdata service, the demand for more and more throughput increases and theradio spectrum becomes more crowded. A new paradigm in wirelesscommunication is emerging where radios can be built to withstandinterference to the level where interference is no longer avoided.Interference is allowed, even invited, to allow for more wirelessdevices to make use of the wireless spectrum. For example, the LTEAdvanced standard (to support the HetNet feature) allows, evenencourages, interference. If this new feature is enabled, reliableperformance would require mobiles to have some kind of interferencemitigation in the receivers.

Beamforming is a signal processing technique used in sensor arrays fordirectional signal transmission or reception. A receiver equipped withan array of antennas can employ any number of digital beamformingalgorithms to direct a strong beam in particular direction (e.g., thedirection of a particular transmitter) while directing deep nulls inother directions.

As may be understood from U.S. Pat. No. 10,091,798, to Learned andKaminski, multiuser detection (MUD) on a channel may be performed usingsequential/successive interference cancellation (SIC). A SIC MUDreceiver estimates received signal parameters for an interfering signal,such as received amplitude, carrier frequency, phase, and baud timing.The receiver then demodulates the interfering signal, recreates it usingthe estimated parameters and demodulated symbol weights, and subtractsit from the received signal to recover (or “reveal”) the signal ofinterest (SOI) underneath. This “cleaned up” received signal is thenpassed to a legacy receiver that works well in the absence of co-channel(same band) interference. U.S. Pat. No. 9,998,199, to Learned and Fiore,describe structures and techniques for use with MUD receivers includingSIC MUD receivers. U.S. Pat. Nos. 10,091,798 and 9,998,199 are herebyincorporated by reference herein in their entireties.

SUMMARY

Performing conventional beamforming followed by successive interferencecancellation (SIC) may be suboptimal. For example, SIC MUD receivers mayperform well when the strength of an interfering signal is actuallysignificantly higher than that of a SOI. Prior attempts at finding asuitable (and, ideally, an optional) MUD-enhancing beam have includediteratively searching and grading beams. It is appreciated herein thatperforming such an exhaustive search of beams can be inefficient and, insome applications, it may be preferable to target a specificsignal-to-interference-plus-noise ratio (SINR) without employing anexhaustive search.

Disclosed herein are techniques for finding, in a closed-form manner, abeam that can achieve a target signal-to-interference-plus-noise ratio(SINR) value without significantly degrading the signal-to-noise ratio(SNR) of a SOI. This approach can result in fewer computations comparedto existing techniques and offers the potential for enhanced performanceand accuracy of a MUD algorithm since an SINR level advantageous for aparticular MUD algorithm and/or operating environment can be directlytargeted.

According to one aspect of the disclosure, in a multi-user detection(MUD) receiver, a method for identifying a beam which produces aspecific signal-to-interference-plus-noise ratio (SINR) includes:determining a maximum output SINR; determining beam weights to achieve atarget SINR using the determined maximum output SINR; applying the beamweights to one or more received signals to generate a beamformed signalhaving the target SINR, one or more of the received signals comprised ofa signal of interest (SOI), one or more interfering signals, and noise;and providing the beamformed signal to a multi-user detection unit torecover the SOI.

According to another aspect of the disclosure, a system can include: aplurality of antenna elements; a front end unit coupled to receivesignals from the plurality of antenna elements and configured to downcovert the received signals, one or more of the down converted signalscomprised of a signal of interest (SOI), one or more interferingsignals, and noise; and a beam determination unit configured todetermine a maximum output SINR determine beam weights to achieve atarget SINR using the determined maximum output SINR; a beamformercoupled to receive the down converted signals from the front end unitand configured to apply the beam weights to the down converted signal togenerate a beamformed signal having the target SINR; and a multi-userdetection (MUD) unit couple to receive the beamformed signal andconfigured to recover the SOI therefrom.

In some embodiments, determining the maximum output SINR can includedetermining beam weights that maximize SINR. In some embodiments,determining the beam weights that maximize SINR can include using atleast one of: minimum variance distortion-less response (MVDR)beamforming; space time adaptive processing (STAP) beamforming; or spacetime frequency adaptive processing (STFAP) beamforming. In someembodiments, determining the maximum output SINR can include estimatingthe maximum output SINR using a closed form solution. In someembodiments, determining the maximum output SINR further can includeapplying the beam weights that maximize SINR to a model of the SOI and amodel of the noise plus interference to determine the maximum outputSINR. In some embodiments, determining the maximum output SINR caninclude: applying the beam weights that maximize SINR to the receivedsignals to obtain signal samples; and determining the maximum outputSINR using the obtained signal samples.

In some embodiments, determining the beam weights to achieve the targetSINR can include: estimating a correlation matrix of noise plusinterference; estimating a steering vector for the SOI; determining afirst basis vector using the correlation matrix and the estimated SOIsteering vector; selecting a second basis vector; determining a targetscale factor based on the target SINR and the estimated maximum outputSINR; transforming a two-dimensional vector containing the target scalefactor using the first and second basis vectors; and determining thebeam weights using the transformed two-dimensional vector and theestimated correlation matrix. In some embodiments, selecting the secondbasis vector can include selecting the second basis vector with theobject of minimally degrading a signal-to-noise ratio (SNR) of the SOIusing a Gram-Schmidt technique. In some embodiments, the multi-userdetection unit is configured to recover the SOI using successiveinterference cancellation (SIC).

BRIEF DESCRIPTION OF THE DRAWINGS

The manner of making and using the disclosed subject matter may beappreciated by reference to the detailed description in connection withthe drawings, in which like reference numerals identify like elements.

FIG. 1 is a diagram showing a communications environment, or network, inwhich the disclosed subject matter can be embodied.

FIG. 2 is a block diagram of receiver system, according to someembodiments of the present disclosure.

FIGS. 3 and 3A are flow diagrams showing processes for computing beamweights to achieve a target the signal-to-interference-plus-noise ratio(SINR) value, according to some embodiments.

The drawings are not necessarily to scale, or inclusive of all elementsof a system, emphasis instead generally being placed upon illustratingthe concepts, structures, and techniques sought to be protected herein.

DETAILED DESCRIPTION

Before describing embodiments of the present disclosure, someintroductory concepts and terminology are explained. Communicating datafrom one location to another requires some form of pathway or mediumbetween the two locations. In telecommunications and computernetworking, a communication channel, or more simply “a channel,” refersto a connection between two locations over a transmission medium. Theconnection may, for example, be a logical connection and thetransmission medium may be, for example, a multiplexed medium such as aradio channel. A channel is used to convey an information signal, forexample a digital bit stream, from one or several sources or sendingnodes (or more simply sources or transmitters) to one or severaldestinations or receiving nodes (or more simply destinations orreceivers). Regardless of the particular manner or technique used toestablish a channel, each channel has a certain capacity fortransmitting information, often measured by its frequency bandwidth inHz or its data rate in bits per second.

Referring to FIG. 1, a communications environment, or network, 100 caninclude a plurality of radios, or nodes, 102 a, 102 b, 102 c, etc. (102generally). While only three nodes 102 are shown in FIG. 1 for clarity,the disclosed subject matter can be applied to environments with anarbitrary number of radios.

In the example of FIG. 1, a first radio 102 a can transmit a signal ofinterest (SOI) 108 to a second radio, or receiver, 102 b. If there wereno other users in the channel, the receiver 102 b would see the SOI 108plus noise 110 generated by the receiver's processing chain, asillustrated by power spectrum 106 a. A third radio 102 c can transmit aninterference signal 112 (i.e., a signal not of interest to receiver 102b), which can be overheard by receiver 102 b. If there were no otherusers in the channel, the receiver 102 b would see the interferencesignal 112 plus noise 110 generated by the receiver's processing chain,as illustrated by power spectrum 106 c. When radios 102 a and 102 c bothtransmit in the same channel, receiver 102 b sees the SOI 108, theinterference signal 112, and noise 110 generated by the receiver'sprocessing chain, as illustrated by power spectrum 106 b. By definition,interference signal 112 occupies the same channel (or “band”), or atleast a portion of the same band, at the same time as SOI 108. In someembodiments, first radio 102 a and third radio 102 c may intentionallytransmit in the same channel. In other embodiments, such channelinterference may be unintentional.

In the simplified example of FIG. 1, first radio 102 a may be referredto as a “radio of interest” from the perspective of second radio 102 b.That is, a “radio of interest” refers to a radio that transmits a SOI.In the case of bidirectional communication, two or more radios can bemutual radios of interest in that they each transmit and receive signalsof interest. Mutual radios of interest are sometimes referred to as a“user” of a channel. An interferer transmitting in the same band may beconsidered a separate user (“interference user”) of the channel.

In a conventional radio, interference may be treated as unstructurednoise, making it difficult if not impossible for the conventional radioto detect a SOI. However, a MUD receiver can allow for successfulcommunication in the same band as an interferer because a MUD receivercan effectively remove interference caused by the interferer and helpthe receiver “see through” that interference in order to detect the SOI.Thus, in some embodiments, receiver 102 b can include a MUD receiverand, more particularly, a SIC MUD receiver. Disclosed embodiments allowfor different radios to operate on the same channel at the same time,allowing users to occupy the same spectrum without having to increasethe bandwidth allocation.

As seen by receiver 102 b, the power of the SOI 108 divided by the sumof the interference power (from all the other interfering signals, suchas signal 112) and the power of the background noise may be referred toas the signal-to-interference-plus-noise ratio (SINR). Different SINRvalues may be more or less favorable to the MUD algorithm withinreceiver 102 b.

Turning to FIG. 2, a receive system 200 according to some embodimentscan include a plurality of antennas elements 202 a, 202 b, . . . , 202 n(202 generally), front end unit 206, a beamformer 208, a closed-formbeam determination unit 210, a MUD unit 212, and a demodulator 214. Asused herein, the “unit” refers to a collection of hardware and/orsoftware configured to perform and execute the processes, steps, orother functionality described in conjunction therewith. Receive system200 can be used to receive a SOI within an interference channel, such asis described above in the context of FIG. 1. While system 200 isdescribed herein as a receive system, the general concepts andstructures described herein can also be implemented within a transmitsystem or a transmit-receive system. The various components of receivesystem 200 can be coupled together as shown in FIG. 2 or in any othersuitable manner.

Antenna elements 202 can intercept propagated electromagnetic (EM)waves, e.g., EM waves propagated by a radio of interest in addition towaves propagated by one or more interferers. In some embodiments,antenna elements 202 may be provided as an antenna array. Antennaelements 202 can be connected to front end unit 206 via signal paths (or“lines”) 216.

Front end unit 206 can include conventional front end radio componentsto capture a received RF signal within a particular RF band. In someembodiments, a wideband front end may be used to capture signals withinmultiple RF bands at the same time. In some embodiments, front end unit206 may down convert and/or digitize the captured RF signals. Front endunit 206 can include, for example, a mixer for baseband sampling, a lowpass filter (LPF), an automatic gain controller (AGC), and an analog todigital (ADC) converter. Front end unit 206 can provide, as output, downconverted signals 218 to beamformer 208. For convenience, a signalcarried on a particular signal path/line may be referred to herein usingthe signal path's reference number shown in FIG. 2. While a single frontend unit 206 is shown in FIG. 2, in other embodiments, different antennaelements 202 can be coupled to different front end units.

Beamformer 208 can amplify the down converted signals 218 according to agiven set of beam weights (e.g., a weighting vector) to achieve adesired directional sensitivity pattern or “beam.” The beam weights canbe calculated by closed-form beam determination unit (“beamdetermination unit” for brevity) 210 and provided to beamformer via line224. The resulting beamformed signal 220 can be provided to MUD unit212. Beamformer 208 can be provided as an analog, digital, or hybridbeamformer. In the case of an analog beamformer, the order of front endunit 206 and beamformer 208 in the receive chain may be swapped.

Beam determination unit 210 can receive a target SINR 222 as input. Beamdetermination unit 210 can then compute beam weights to achieve thetarget SINR 222 according to the processes described below in thecontext of FIGS. 3 and 3A. The target SINR 222 can be user-defined orselected based on the type of MUD algorithm used or other operational orapplication-specific factors. For example, target SINR 222 can be valuethat enables MUD unit 212 to recover a SOI in the presence of one ormore interfering signals.

MUD unit 212 can be configured to perform multi-user detection (MUD) onthe beamformed signal 220 using one or more MUD processing techniques oralgorithms. In some embodiments, SIC MUD unit 212 may be configured toperform sequential interference cancellation (SIC). In some embodiments,MUD unit 212 can estimate received signal parameters for one or moreinterfering signals, such as received amplitude, carrier frequency,phase, and baud timing. The MUD unit 212 can demodulate the interferingsignals and recreate them using the estimated parameters and demodulatedsymbol weights. Using the estimated signal parameters and demodulatedsymbols, MUD unit 212 can create an estimate of the received interferingsignals and subtract them from the received signal to reveal a SOIunderneath. This “cleaned up” received signal 226 can then be passed todemodulator 214.

Demodulator 214 can include circuitry to receive and demodulate thecleaned up signal 226 and, in response, generate a demodulated anddecoded bit stream 228 as output. Decoded bit stream 228 can representdecoded packets/frames associated with the transmission from a radio ofinterest. The decoded bit stream 228 can be processed by additionalhardware and/or software components of a receiver not shown in FIG. 2.

FIGS. 3 and 3A shows illustrative processes for computing beam weightsto achieve a target the signal-to-interference-plus-noise ratio (SINR).The illustrated processing can be implemented within a radio system,such as receive system 200 of FIG. 2. In some embodiments, theprocessing can be implemented within a closed-form beam determinationunit, such as unit 210 of FIG. 2. Blocks within the flow diagramsrepresent steps that can be performed by computer software instructionsor by functionally equivalent circuits such as a digital signalprocessor (DSP) circuit or an application specific integrated circuit(ASIC). The sequence of blocks in a flow diagram is merely illustrativeand, unless otherwise stated, the functions represented by the blockscan be performed in any convenient or desirable order.

A mathematical model for two simultaneously transmitted narrowbandsignals arriving on each of M antennas is as follows:

$\begin{matrix}{{{r_{1}(t)} = {{A_{1,1}e^{2\pi i\theta_{1,1}}{s_{1}(t)}} + {A_{1,2}e^{2\pi i\theta_{1,2}}{s_{2}(t)}} + {n_{1}(t)}}}{{r_{2}(t)} = {{A_{2,1}e^{2\pi i\theta_{2,1}}{s_{1}(t)}} + {A_{2,2}e^{2\pi i\theta_{2,2}}{s_{2}(t)}} + {n_{2}(t)}}}\vdots{{{r_{M}(t)} = {{A_{M,1}e^{2\pi i\theta_{M,1}}{s_{1}(t)}} + {A_{M,2}e^{2\pi i\theta_{M,2}}{s_{2}(t)}} + {n_{M}(t)}}},}} & (1)\end{matrix}$

where r_(i)(t) denotes the signal received at antenna i, s_(j)(t)denotes the unit-amplitude signal transmitted by terminal j, A_(i,j)denotes the amplitude of the signal s_(j)(t) on antenna i, θ_(i,j)denotes the phase offset of s_(j)(t) due to the time delay of the signalto antenna i, and n_(i)(t) denotes the Additive White Gaussian Noise(AWGN) noise process with variance σ_(i) ² on antenna i.

For simplicity in what follows, it is assumed that the noise isindependent across antenna elements (e.g., antenna elements 202 in FIG.2) and that the noise power spectral density is the same over allantennas. The noise variance is denoted as σ². Re-writing the model in(1) in vector notation givesr(t)=a ₁ s ₁(t)+a ₂ s ₂(t)+n(t),  (2)wherer(t)=[r ₁(t),r ₂(t), . . . ,r _(M)(t)]^(T),a _(j)(t)=[A _(1,j) e ^(2πiθ) ^(i,j) ,A _(2,j) e ^(2πiθ) ^(2,j) , . . .,A _(M,j) e ^(2πiθ) ^(M,j) ]^(T),andn(t)=[n ₁(t),n ₂(t), . . . ,n _(M)(t)]^(T).

The normal direction of the plane wave corresponding to the signals_(j)(t) (in some specified reference coordinate system) can be denotedby the 3×1 unit-norm vector ϕ_(j), and the locations of the M antennaelements can be denoted by the 3×1 vectors x₁, x₂, . . . , x_(M) (in thesame coordinate system). Then, the angles in (2) are given by

$\begin{matrix}{{\theta_{i,j} = \frac{2\pi{f_{c}\left( {\phi_{j} \cdot x_{i}} \right)}}{c}},} & (3)\end{matrix}$where f_(c) denotes the carrier frequency, and c denotes the speed oflight.

Beamforming can be described as applying an M×1 weight vector w to thereceived signal r(t) to obtain the output signal:w ^(H) r(t).where the operator (·)^(H) is the Hermitian complex conjugate transposeoperation. Conventionally, this weight vector can be is chosen tomaximize gain on a SOI while reducing gain on an interfering signal(i.e. maximizing SINR). While the disclosed subject matter allowstargeting arbitrary SINR values (e.g., SINR values that are favorable toa MUD algorithm), a description of a closed-form solution to achievingmaximum SINR is first discussed.

Because the derivation above assumes a narrowband signal, the carrierfrequency f_(c) is taken to be a fixed constant instead of ranging overthe bandwidth of the signal. While the approach below is described for anarrowband signal, the overall approach also applies to a widebandsignal using an enhanced correlation matrix employing time and/orDoppler taps.

Suppose that s₁(t) is the SOI with steering vector a₁, that s₂(t) is aninterfering signal with steering vector a₂, and that the noise varianceis σ². Then, the correlation matrix of the noise plus interference isgiven byR=a ₂ a ₂ ^(H)+σ² I,where I denotes the M×M identity matrix. The correlation matrix Rindicates the correlation of noise plus interference across multipleantenna elements

In practice, correlation matrix R or an equivalent may be estimated.Examples of considerations and techniques for estimating this matrix aredescribed in the following reference, which is hereby incorporated byreference in its entirety: [1] Y. Gu and A. Leshem, “Robust AdaptiveBeamforming Based on Interference Covariance Matrix Reconstruction andSteering Vector Estimation,” in IEEE Transactions on Signal Processing,vol. 60, no. 7, pp. 3881-3885, July 2012, doi: 10.1109/TSP.2012.2194289.In the following sections, an estimate of R is assumed irrespective ofthe particular method with which it is estimated.

Maximizing the SINR is equivalent to maximizing the following ratio:

$\begin{matrix}{\frac{{{\left( {R^{1/2}w} \right)^{H}\left( {R^{{- 1}/2}a_{1}} \right)}}^{2}}{{{R^{1/2}w}}^{2}}.} & (4)\end{matrix}$

This ratio arises from “whitening” the original problem. In particular,suppose that the received signal vector is given byr(t)=a ₁ s ₁(t)+a ₂ s ₂(t)+n(t),where a₁ denotes the array steering vector associated with the SOI, a₂denotes the array steering vector associated with the interference, andn(t) denotes the AWGN noise. Multiplying equation (2) by R^(−1/2) yieldsR ^(−1/2)×(t)=R ^(−1/2) a ₁ s ₁(t)+[R ^(−1/2)(a ₂ s ₂(t)+n(t))].  (5)

As a result, the autocorrelation matrix of the bracketed terms is adiagonal matrix. With this setup, the weight can be easily chosen.

Equation (4) is maximized when R^(1/2)w=R^(−1/2)a₁, so setting w=R⁻¹a₁maximizes the output SINR.

The preceding description relates to maximizing the output SINR, whichcan be seen as the conventional approach to beamforming. However, aspreviously discussed, this conventional approach may be suboptimal whenthe resulting beamformed signal is subsequently processed using a MUDalgorithm such as SIC.

A target SINR can be defined as a scalar multiple (or “scale factor”) ofthe maximum SINR (e.g., ½ the maximum SINR). If R^(−1/2)a₁=[1,0,0]^(T),then setting R^(1/2)w=[1/√{square root over (2)}, 1/√{square root over(2)}, 0]^(T) can achieve half of the maximum SINR at the output of thebeamformer. This is the approach taken with a change-of-basisimplementation.

As in equation (5), the signal steering vector a₁ is multiplied by thewhitening matrix R^(−1/2). Denote the resulting vector by â₁. Setb₁=â₁/∥â₁∥² as the first basis vector in the new basis. In this newbasis, â₁ can be represented as [1, 0, . . . , 0]^(T) as desired. Toobtain the second element of the basis, in some embodiments, theGram-Schmidt method with R^(1/2)a₁ as its input can be utilized.Subtracting off the component of R^(1/2)a₁ that lies along the directiongiven by â₁ yieldsb=R ^(1/2) a ₁−(â ₁ ^(H) a ₁)â ₁.

In other embodiments, the Gram-Schmidt method can be initialized withthe eigenvector corresponding to the largest eigenvalue of R. Next, thefollowing can be normalized:

$b_{2} = \frac{b}{{b}^{2}}$

The first two elements of the new basis are b₁ and b₂, which can becombined into a matrixB=[b ₁ ,b ₂].

Suppose that ½ of the maximum SINR is the target. Then, letR^(1/2)w=B[1/√{square root over (2)}, 1/√{square root over (2)}]^(T),where multiplying by B transforms [1/√{square root over (2)}, 1/√{squareroot over (2)}, 0, . . . , 0]^(T) into the new basis. To produce a finalset of beam weights, multiply B[1/√{square root over (2)}, 1/√{squareroot over (2)}]^(T) by R^(−1/2).

Turning to FIG. 3, a process 300 can begin at block 304 by estimating,measuring, or otherwise obtaining a maximum output SINR. The maximumoutput SINR is the SINR achieved when the beamformer weighting isselected to maximize gain on a SOI while reducing gain on one or moreinterfering signals. An a priori or an a posteriori method can be usedto determine the maximum output SINR.

A priori, the maximum output SINR can be estimated/predicted by applyingderived SINR-maximizing beam weights to the modeled SOI (e.g., a₁s₁(t)above) and modeled noise plus interference (e.g., a₂s₂ (t) and n(t)above), and taking the ratio of the power of the respective outputs. TheSINR-maximizing beam weights are the beam weights that maximize gain ona SOI while reducing gain on one or more interfering signals. TheSINR-maximizing beam weights can be derived using a known technique suchas minimum variance distortion-less response (MVDR) beamforming, spacetime adaptive processing (STAP) beamforming, or space time frequencyadaptive processing (STFAP) beamforming. The a priori approach may bereferred to as a closed form solution to determining the maximum outputSINR.

A posteriori, the maximum output SINR can be determined by applying theSINR-maximizing beam weights to a received signal and then taking samplemeasurements (e.g., from digital samples of signals 218 in FIG. 2). Thisapproach measures the actual SINR after applying the beam weights.Assuming the interference is Gaussian, a technique such asmaximum-likelihood (ML) estimation or second- and fourth-order moments(M2M4) estimation can be used to determine the maximum output SINR. Forsome modulation formats, known cyclostationary methods can be employedto estimate SINR after applying beam weights, such as those described inthe following reference, which is hereby incorporated by reference inits entirety: [2] F. Mazzenga and F. Vatalaro, “Parameter estimation inCDMA multiuser detection using cyclostationary statistics,” inElectronics Letters, vol. 32, no. 3, pp. 179-181, 1 Feb. 1996, doi:10.1049/el:19960167.

At block 306, a target SINR can be determined. As previously discussed,the target SINR can be user-defined or selected based on the type of MUDalgorithm used or other operational or application-specific factors. Insome embodiments, techniques described in U.S. Pat. No. 9,998,199 can beused to determine a target SINR for a particular MUD algorithm.

Block 308 includes determining beam weights (e.g., a vector of weights)to achieve the target SINR. The beam weights can be computed using atarget scale factor, which is based on the target SINR. In particular,the target scale factor can be calculated from the estimated maximumoutput SINR (block 304) and the target SINR (block 306). Detailedprocedures for computing beam weights to achieve the target SINR aredescribed above and also below in the context of FIG. 3A.

At block 310, the beam weights can be applied to a received signal toachieve the target SINR. For example, referring briefly to FIG. 2, beamweights 224 can be used by beamformer 208 to amplify down convertedsignals 218, resulting in a beamformed signal 220 that has the targetSINR.

At block 312, the beamformed signal can be provided to a MUD unit, suchas MUD unit 212 of FIG. 2.

FIG. 3A shows an illustrative process 340 for determining beam weightsto achieve the target SINR. Process 340 can be used in combination withprocess 300 (e.g., process 340 can be performed as part of block 308).

At block 342, a correlation matrix of noise plus interference (e.g.,matrix R above) can be estimated using a known technique, as previouslydiscussed.

At block 344, a steering vector for the SOI (e.g., vector a₁ above) canbe estimated. In some embodiments, the SOI's steering vector can beestimated using pilot symbols, angle-of-arrival estimation, or othertechnique known in the art.

At block 346, a first basis vector (e.g., vector b₁ above) can bedetermined using the correlation matrix and the estimated SOI steeringvector. In particular, the first basis vector (b₁) can be calculated bymultiplying the inverse square root of the correlation matrix (R^(−1/2))by the estimated SOI steering vector (a₁).

At block 348, a second basis vector (e.g., vector b₂ above) can beselected with the object of minimally degrading the SNR of the SOI. Thiscan be accomplished using one of the Gram-Schmidt-based techniquesdescribed above, or any other method designed to minimally degrade thesignal-of-interest SNR while targeting a particular SINR value. The twobasis vectors can be combined into a matrix (e.g., matrix B above).

At block 350, a 2-dimension vector containing the target scale factor(e.g., the scale factor calculated from blocks 304 and 306, aspreviously discussed) can be formed. This 2-dimensional vector can betransformed into the basis represented by the first and second basisvectors or, equivalently, by basis matrix (B). Additional details forperforming this transformation are provided above.

At block 352, the beam weights can be determined using the transformed2-dimensional vector, from block 350, and the estimated correlationmatrix, from block 342. For example, the beam weights can be calculatedby multiplying the transformed 2-dimensional vector by the inversesquare root of the correlation matrix (R^(−1/2)).

It is recognized that processes 300 and 340 can result in a beamformedsignal that achieves a target SINR (e.g., a MUD-favorable SINR) withoutsignificantly degrading the SNR of the SOI.

Disclosed embodiments may be implemented in any of a variety ofdifferent forms. For example, disclosed embodiments can be implementedwithin various forms of communication devices, both wired and wireless,such as television sets, set top boxes, audio/video devices,smartphones, laptop computers, desktop computers, tablet computers,satellite communicators, cameras having communication capability,network interface cards (NICs) and other network interface structures,base stations, access points, and modems.

The subject matter described herein can be implemented in digitalelectronic circuitry, or in computer software, firmware, or hardware,including the structural means disclosed in this specification andstructural equivalents thereof, or in combinations of them. The subjectmatter described herein can be implemented as one or more computerprogram products, such as one or more computer programs tangiblyembodied in an information carrier (e.g., in a machine-readable storagedevice), or embodied in a propagated signal, for execution by, or tocontrol the operation of, data processing apparatus (e.g., aprogrammable processor, a computer, or multiple computers). A computerprogram (also known as a program, software, software application, orcode) can be written in any form of programming language, includingcompiled or interpreted languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or another unit suitable for use in a computing environment.A computer program does not necessarily correspond to a file. A programcan be stored in a portion of a file that holds other programs or data,in a single file dedicated to the program in question, or in multiplecoordinated files (e.g., files that store one or more modules, subprograms, or portions of code). A computer program can be deployed to beexecuted on one computer or on multiple computers at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification, includingthe method steps of the subject matter described herein, can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions of the subject matter describedherein by operating on input data and generating output. The processesand logic flows can also be performed by, and apparatus of the subjectmatter described herein can be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processor of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for executing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto-optical disks, or optical disks. Information carrierssuitable for embodying computer program instructions and data includeall forms of nonvolatile memory, including by ways of examplesemiconductor memory devices, such as EPROM, EEPROM, flash memorydevice, or magnetic disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

In the foregoing detailed description, various features are groupedtogether in one or more individual embodiments for the purpose ofstreamlining the disclosure. This method of disclosure is not to beinterpreted as reflecting an intention that each claim requires morefeatures than are expressly recited therein. Rather, inventive aspectsmay lie in less than all features of each disclosed embodiment.

The disclosed subject matter is not limited in its application to thedetails of construction and to the arrangements of the components setforth in the following description or illustrated in the drawings. Thedisclosed subject matter is capable of other embodiments and of beingpracticed and carried out in various ways. As such, those skilled in theart will appreciate that the conception, upon which this disclosure isbased, may readily be utilized as a basis for the designing of otherstructures, methods, and systems for carrying out the several purposesof the disclosed subject matter. Therefore, the claims should beregarded as including such equivalent constructions insofar as they donot depart from the spirit and scope of the disclosed subject matter.

Although the disclosed subject matter has been described and illustratedin the foregoing exemplary embodiments, it is understood that thepresent disclosure has been made only by way of example, and thatnumerous changes in the details of implementation of the disclosedsubject matter may be made without departing from the spirit and scopeof the disclosed subject matter.

The invention claimed is:
 1. In a multi-user detection (MUD) receiver, amethod for identifying a beam which produces a specificsignal-to-interference-plus-noise ratio (SINR), the method comprising:determining a maximum output SINR; determining beam weights to achieve atarget SINR using the determined maximum output SINR; applying the beamweights to one or more received signals to generate a beamformed signalhaving the target SINR, one or more of the received signals comprised ofa signal of interest (SOI), one or more interfering signals, and noise;and providing the beamformed signal to a multi-user detection unit torecover the SOI.
 2. The method of claim 1, wherein determining themaximum output SINR comprises determining beam weights that maximizeSINR.
 3. The method of claim 2, wherein determining the beam weightsthat maximize SINR includes using at least one of: minimum variancedistortion-less response (MVDR) beamforming; space time adaptiveprocessing (STAP) beamforming; or space time frequency adaptiveprocessing (STFAP) beamforming.
 4. The method of claim 2, whereindetermining the maximum output SINR includes estimating the maximumoutput SINR using a closed form solution.
 5. The method of claim 2,wherein determining the maximum output SINR further comprises applyingthe beam weights that maximize SINR to a model of the SOI and a model ofthe noise plus interference to determine the maximum output SINR.
 6. Themethod of claim 2, wherein determining the maximum output SINRcomprises: applying the beam weights that maximize SINR to the receivedsignals to obtain signal samples; and determining the maximum outputSINR using the obtained signal samples.
 7. The method of claim 1,wherein determining the beam weights to achieve the target SINRcomprises: estimating a correlation matrix of noise plus interference;estimating a steering vector for the SOI; determining a first basisvector using the correlation matrix and the estimated SOI steeringvector; selecting a second basis vector; determining a target scalefactor based on the target SINR and the estimated maximum output SINR;transforming a two-dimensional vector containing the target scale factorusing the first and second basis vectors; and determining the beamweights using the transformed two-dimensional vector and the estimatedcorrelation matrix.
 8. The method of claim 7, wherein selecting thesecond basis vector comprises selecting the second basis vector with theobject of minimally degrading a signal-to-noise ratio (SNR) of the SOIusing a Gram-Schmidt technique.
 9. The method of claim 1, wherein themulti-user detection unit is configured to recover the SOI usingsuccessive interference cancellation (SIC).
 10. A system comprising: aplurality of antenna elements; a front end unit coupled to receivesignals from the plurality of antenna elements and configured to downcovert the received signals, one or more of the down converted signalscomprised of a signal of interest (SOI), one or more interferingsignals, and noise; a beam determination unit configured to: determine amaximum output SINR, and determine beam weights to achieve a target SINRusing the determined maximum output SINR; a beamformer coupled toreceive the down converted signals from the front end unit andconfigured to apply the beam weights to the down converted signal togenerate a beamformed signal having the target SINR; and a multi-userdetection (MUD) unit couple to receive the beamformed signal andconfigured to recover the SOI therefrom.
 11. The system of claim 10,wherein determining the maximum output SINR comprises determining beamweights that maximize SINR.
 12. The system of claim 11, whereindetermining the beam weights that maximize SINR includes using at leastone of: minimum variance distortion-less response (MVDR) beamforming;space time adaptive processing (STAP) beamforming; or space timefrequency adaptive processing (STFAP) beamforming.
 13. The system ofclaim 11, wherein determining the maximum output SINR includesestimating the maximum output SINR using a closed form solution.
 14. Thesystem of claim 11, wherein determining the maximum output SINR furthercomprises applying the beam weights that maximize SINR to a model of theSOI and a model of the noise plus interference to determine the maximumoutput SINR.
 15. The system of claim 11, wherein determining the maximumoutput SINR comprises: applying the beam weights that maximize SINR tothe received signal to obtain signal samples; and determining themaximum output SINR using the obtained signal samples.
 16. The system ofclaim 10, wherein determining the beam weights to achieve the targetSINR comprises: estimating a correlation matrix of noise plusinterference; estimating a steering vector for the SOI; determining afirst basis vector using the correlation matrix and the estimated SOIsteering vector; selecting a second basis vector; determining a targetscale factor based on the target SINR and the estimated maximum outputSINR; transforming a two-dimensional vector containing the target scalefactor using the first and second basis vectors; and determining thebeam weights using the transformed two-dimensional vector and theestimated correlation matrix.
 17. The method of claim 16, whereinselecting the second basis vector comprises selecting the second basisvector with the object of minimally degrading a signal-to-noise ratio(SNR) of the SOI using a Gram-Schmidt technique.
 18. The system of claim10, wherein the multi-user detection unit is configured to recover theSOI using successive interference cancellation (SIC).